Know calibration methods for robot systems generally involve artificial teaching. For example, an operator manually controls a robot of the robot system to move an end execution tool mounted on a flange of the robot to reach the same target point with a plurality of different poses (for a 6-axis robot, generally with four or more different poses). The operator must visually determine whether the tool is moved to the same target point, and consequently, calibration errors arise leading to inaccurate tool usage. A transformation matrix of the center of an end execution tool with respect to the center of the flange of the robot is inaccurate. Furthermore, it is extremely time-consuming to repeatedly manually control the robot to reach the same target point and visually verify the movement, greatly decreasing work efficiency. Moreover, the robot system must be re-calibrated every time the end execution tool is replaced, adding to the time burden.
It is also known to automatically calibrate a robot system based on a calibrated vision sensor. In the automatic calibration method, the robot is controlled to move the center of the end execution tool mounted on the flange of the robot to the same one target point in various different poses. The automatic calibration method greatly saves time and effort compared with the method of visually judging whether the end execution tool is moved to the target point. However, in the known automatic calibration method, it is necessary to identify the center of the end execution tool using the vision sensor. Generally, the end execution tool has a very complex geometric structure and it is difficult to identify the center of the end execution tool. More particularly, when frequent replacement of the end execution tool is necessary, the vision sensor needs to re-identify the center of the end execution tool every time the end execution tool is replaced, which is also very troublesome and time-consuming.